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Corporate Credit Rating Systems

Dates:
October 10 - 11, 2017
Price:
EUR 1,400
Location:
Prague, NH Hotel Prague
Language:
English
Lecturer:
Dr. Krassimir Kostadinov
    Key points / questions answered:
  • Which rating models are appropriate for regulations such as Basel III or IFRS 9?
  • How to gather, structure and maintain the data needed for credit ratings of corporate entities?
  • How to handle data availability and data quality challenges in practice?
  • Which statistical tools and rating model development practices are robust and proven?
  • Which innovative methods might help the bank to obtain forward-looking risk assessments?
  • How to create a rating system that is useful for risk-adjusted pricing in long-term corporate customer-bank relationships?
The purpose of this seminar is to introduce you to the key methodologies to design, develop, calibrate and validate credit rating systems for corporate customers.

We start with an overview and discussion of the three main types of credit rating systems: the Early Warning systems, the Long-term Corporate (issuer) Ratings and 'Master Scale'-based Rating systems. Particular focus is put on the uses and misuses of each of the three system types, including their applicability to meet regulatory requirements, such as Basel III or IFRS 9, and their appropriateness to address business-related objectives, such as risk-adjusted pricing or operational risk management.

We then take a closer look at the 'Master Scale'-based Rating systems. 'Master scales' allocate a non-overlapping range of probabilities of default (PD) that are stable over time to each rating class. The rating methods for such systems need to produce accurate projections of the 1-year PD based on actually observed defaults. Starting from 'simpler' questions, such as what constitutes a default of a corporate customer, how to handle groups of legally or economically related entities from a data management perspective or how to build and maintain an appropriate historic record of defaults, we gradually dig into the core quantitative modelling methodology. Covered topics include statistical analysis of Corporate Balance Sheet KPIs, design and development of Integrated Rating Models based on quantitative factors and qualitative assessments, and model validation techniques. Throughout this part of the course we give practical advice and examples related to common challenges such as low default portfolios, missing / incomplete data and input data outliers.

After this, we turn to the Early Warning systems, which help the bank to identify reliably upcoming defaults or substantial credit risk increases of specific corporate customers on an ongoing basis. Customer account behaviour variables as well as expert opinions begin to play a critically important role in the risk differentiation mechanics within such system, making the risk assessment a fully dynamic process. Building upon that, we present a generic framework to assess the impact of additional observable factors, such as market prices or macroeconomic indices, on the corporate customer's credit risk in a forward-looking manner.

Finally, we look at the Long-term Corporate (issuer) Ratings which express risk in relative rank order (i.e. they are ordinal measures of credit risk) and are not predictive of a specific frequency of default. The rating model development in this case needs to start from a specific industrial sector and only thereafter to combine the multiple specific models unto a unified rating scale. We present a an example of a Long-term Corporate Rating model within the healthcare sector and illustrate the process of mapping the model's results to a generic rating scale, such as S&P or Moody's.

09.15 - 12.00

Overview of the Credit Rating Systems

  • Corporate Customers: definition and main characteristics
  • The three main types of Credit Rating Systems
  • Relationship with regulatory requirements
    • Basel III: Internal Rating-Based Approach
    • IFRS 9: Expected Loss model
  • Business process objectives
    • Using ratings for risk-adjusted pricing
    • Using ratings for operational decision-taking

The Master Scale Ratings

  • What is a Master Scale?
  • Defining the corporate entities to be rated
    • Groups of related customers
    • Corporate structures and customer account management over time
    • Default definitions
    • Data model example

12.00 - 13.00 Lunch

13.00 - 16.30 The Master Scale Ratings (continued)

  • The Balance Sheet KPIs
    • From Long List to Short List KPIs
    • Handling of incomplete data
    • Handling of outliers
    • Quantitative Model development
    • Model validation
  • Integrated Rating Models
    • Quantification of qualitative assessments
    • Bringing in account behaviour variables: pros & cons
    • Low default portfolios
    • Small sample sizes
  • Exercises and Q&A

Wednesday, October 11

09.00 - 09.15 Recap

09.15 - 12.00 Early Warning Systems

  • How to assess imminent credit risk?
    • Corporate customer behaviour variables
    • Early risk identification as an organisational challenge
    • Integrating expert opinions and quantitative rating models
    • Expected Loss vs. Probability of Default metrics
  • How to obtain forward-looking credit risk scores?
    • Generic framework for incorporation of additional observable variables
    • Case Study: improving an Early Warning system using market prices
    • Case Study: implementing the IFRS 9 Expected Loss model

12.00 - 13.00 Lunch

13.00 - 16.30 Long-term Corporate Ratings

  • Understanding ordinal risk measurement
    • Case study: Long-term ratings for corporate customers in the healthcare sector
    • Mapping internal ratings to a Rating Agency scale
  • Exercises and Q&A

Evaluation and Termination of the Seminar

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